Geospatial data representing real-world locations are three-dimensional (3-D), and modern measurement systems collect data in a physical 3-D environment. Time as the fourth dimension is acknowledged, but this topic focuses on 3-D data. This topic defines and describes the global spatial data model (GSDM) as a collection of mathematical concepts and procedures that can be used to collect, organize, store, process, manipulate, evaluate, and use 3-D spatial data. Measurements of quantities such as angles, length, time, current, mass, and temperature are used with known physical and geometrical relationships to compute spatial data components that are stored for subsequent use and reuse.
In the past, records of such measurements were written in field logs, or journals, and the spatial information was compiled into an analog map that typically served two purposes. The map was simultaneously the primary storage medium for the spatial information and the end product of the data collection process. Spatial data are now collected, stored, and manipulated digitally in an electronic environment, and the primary storage medium is rarely the end product. Instead, the same digital data file can be duplicated repeatedly and used to generate and/or support many different spatial data products. In either case, whether developing an analog or digital spatial data product, algorithms are the mathematical rules used to manipulate measurements and spatial data to obtain meaningful spatial information. In addition, the quality of spatial information is dependent upon the quality of the original measurement, completeness of the required information, and appropriateness of the algorithms used to manipulate the data.
The GSDM includes both the algorithms for processing spatial data and the procedures that can be used to provide a defensible statistical description of spatial data quality. That means measurement professionals can focus on building and/or using systems that generate reliable spatial data components and spatial data users in various disciplines can devote attention to using and interpreting the data with the assurance that all parties generating and/or using the data are “on the same page” (i.e., using a common spatial data model).
This first topic is a summary of the defining document for the GSDM.The intent is to cite primary works because other people developed most of the concepts described herein. Leick (2004) defines the 3-D geodetic model of which the GSDM is a part, Mikhail (1976) provides a comprehensive discussion of functional and stochastic models, and, when discussing models,
Moritz (1978) comments on the simplicity of using the basic global rectangular XIYIZ system without an ellipsoid. When the aforementioned concepts are combined in a systematic way with particular attention to the manner in which spatial data are used, the synergistic whole—the GSDM—appears to be greater than the sum of the parts.
Neither is the GSDM concept a new one. Seeber (1993) states that H. Burns proposed the concept of a global three-dimensional polyhedron network as early as 1878. The differences now are that the Global Positioning System (GPS) and other modern technologies have made a global network practical and that the polyhedron need not be limited to Earth-based points. The GSDM might also be an appropriate model for describing the “best” instantaneous positions of a global network of continuously operating reference stations (CORS) computed in real time with respect to the International Terrestrial Reference Frame (ITRF). An adopted mean position for each CORS may serve the needs of most users, but corrections for short-term variations caused by the Earth’s tides, long-term continental drift velocities, and even catastrophic events such as earthquakes should be available to those needing them. It is readily acknowledged that such policies are already being used in the scientific community and that a space-fixed inertial reference system is more appropriate for describing the motion of Earth satellites. The GSDM should not be viewed as a prescriptive model, but as an inclusive model that accommodates the diverse practice of many spatial data users and provides an efficient bridge between local “flat-Earth” uses and rigorous scientific applications.
